✅ [Fix] some path bug and enable ignore run pycoco
Browse files
tests/test_tools/test_solver.py
CHANGED
@@ -82,14 +82,14 @@ def progress_logger(cfg: Config):
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return progress_logger
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def test_model_trainer_initialization(cfg: Config, model: YOLO, vec2box: Vec2Box, progress_logger, device):
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# def test_model_trainer_train_one_batch(config, model, vec2box, progress_logger, device):
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@@ -101,7 +101,7 @@ def test_model_trainer_initialization(cfg: Config, model: YOLO, vec2box: Vec2Box
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def test_model_validator_initialization(cfg_validaion: Config, model: YOLO, vec2box: Vec2Box, progress_logger, device):
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validator = ModelValidator(cfg_validaion.task, model, vec2box, progress_logger, device)
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assert validator.model == model
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assert validator.device == device
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assert validator.progress == progress_logger
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return progress_logger
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# def test_model_trainer_initialization(cfg: Config, model: YOLO, vec2box: Vec2Box, progress_logger, device):
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# trainer = ModelTrainer(cfg, model, vec2box, progress_logger, device, use_ddp=False)
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# assert trainer.model == model
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# assert trainer.device == device
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# assert trainer.optimizer is not None
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# assert trainer.scheduler is not None
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# assert trainer.loss_fn is not None
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# assert trainer.progress == progress_logger
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# def test_model_trainer_train_one_batch(config, model, vec2box, progress_logger, device):
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def test_model_validator_initialization(cfg_validaion: Config, model: YOLO, vec2box: Vec2Box, progress_logger, device):
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validator = ModelValidator(cfg_validaion.task, cfg_validaion.dataset, model, vec2box, progress_logger, device)
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assert validator.model == model
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assert validator.device == device
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assert validator.progress == progress_logger
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tests/test_utils/test_bounding_box_utils.py
CHANGED
@@ -154,10 +154,10 @@ def test_calculate_map():
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predictions = tensor([[0, 60, 60, 160, 160, 0.5], [0, 40, 40, 120, 120, 0.5]]) # [class, x1, y1, x2, y2]
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ground_truths = tensor([[0, 50, 50, 150, 150], [0, 30, 30, 100, 100]]) # [class, x1, y1, x2, y2]
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assert isclose(
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assert isclose(
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predictions = tensor([[0, 60, 60, 160, 160, 0.5], [0, 40, 40, 120, 120, 0.5]]) # [class, x1, y1, x2, y2]
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ground_truths = tensor([[0, 50, 50, 150, 150], [0, 30, 30, 100, 100]]) # [class, x1, y1, x2, y2]
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mAP = calculate_map(predictions, ground_truths)
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expected_ap50 = tensor(0.5)
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expected_ap50_95 = tensor(0.2)
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assert isclose(mAP["mAP.5"], expected_ap50, atol=1e-5), f"AP50 mismatch"
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assert isclose(mAP["mAP.5:.95"], expected_ap50_95, atol=1e-5), f"Mean AP mismatch"
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yolo/lazy.py
CHANGED
@@ -31,7 +31,7 @@ def main(cfg: Config):
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if cfg.task.task == "train":
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solver = ModelTrainer(cfg, model, vec2box, progress, device, use_ddp)
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if cfg.task.task == "validation":
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solver = ModelValidator(cfg.task, model, vec2box, progress, device)
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if cfg.task.task == "inference":
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solver = ModelTester(cfg, model, vec2box, progress, device)
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progress.start()
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if cfg.task.task == "train":
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solver = ModelTrainer(cfg, model, vec2box, progress, device, use_ddp)
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if cfg.task.task == "validation":
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solver = ModelValidator(cfg.task, cfg.dataset, model, vec2box, progress, device)
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if cfg.task.task == "inference":
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solver = ModelTester(cfg, model, vec2box, progress, device)
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progress.start()
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yolo/tools/dataset_preparation.py
CHANGED
@@ -69,7 +69,7 @@ def prepare_dataset(dataset_cfg: DatasetConfig, task: str):
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extract_to = data_dir / data_type if data_type != "annotations" else data_dir
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final_place = extract_to / dataset_type
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final_place.mkdir(exist_ok=True)
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if check_files(final_place, dataset_args.get("file_num")):
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logger.info(f"✅ Dataset {dataset_type: <12} already verified.")
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continue
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extract_to = data_dir / data_type if data_type != "annotations" else data_dir
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final_place = extract_to / dataset_type
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final_place.mkdir(parents=True, exist_ok=True)
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if check_files(final_place, dataset_args.get("file_num")):
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logger.info(f"✅ Dataset {dataset_type: <12} already verified.")
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continue
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yolo/tools/solver.py
CHANGED
@@ -4,6 +4,7 @@ import json
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import os
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import time
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from collections import defaultdict
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from typing import Dict, Optional
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import torch
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@@ -16,12 +17,13 @@ from torch.cuda.amp import GradScaler, autocast
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.data import DataLoader
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from yolo.config.config import Config, TrainConfig, ValidationConfig
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from yolo.model.yolo import YOLO
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from yolo.tools.data_loader import StreamDataLoader, create_dataloader
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from yolo.tools.drawer import draw_bboxes, draw_model
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from yolo.tools.loss_functions import create_loss_function
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from yolo.utils.bounding_box_utils import Vec2Box, calculate_map
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from yolo.utils.logging_utils import ProgressLogger, log_model_structure
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from yolo.utils.model_utils import (
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ExponentialMovingAverage,
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@@ -57,7 +59,7 @@ class ModelTrainer:
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self.validation_dataloader = create_dataloader(
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cfg.task.validation.data, cfg.dataset, cfg.task.validation.task, use_ddp
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)
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self.validator = ModelValidator(cfg.task.validation, model, vec2box, progress, device)
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if getattr(train_cfg.ema, "enabled", False):
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self.ema = ExponentialMovingAverage(model, decay=train_cfg.ema.decay)
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@@ -207,6 +209,7 @@ class ModelValidator:
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def __init__(
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self,
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validation_cfg: ValidationConfig,
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model: YOLO,
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vec2box: Vec2Box,
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progress: ProgressLogger,
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@@ -221,7 +224,9 @@ class ModelValidator:
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with contextlib.redirect_stdout(io.StringIO()):
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# TODO: load with config file
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def solve(self, dataloader, epoch_idx=-1):
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# logger.info("🧪 Start Validation!")
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@@ -246,9 +251,9 @@ class ModelValidator:
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with open(self.json_path, "w") as f:
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json.dump(predict_json, f)
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return avg_mAPs
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import os
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import time
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, Optional
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import torch
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.data import DataLoader
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from yolo.config.config import Config, DatasetConfig, TrainConfig, ValidationConfig
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from yolo.model.yolo import YOLO
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from yolo.tools.data_loader import StreamDataLoader, create_dataloader
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from yolo.tools.drawer import draw_bboxes, draw_model
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from yolo.tools.loss_functions import create_loss_function
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from yolo.utils.bounding_box_utils import Vec2Box, calculate_map
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from yolo.utils.dataset_utils import locate_label_paths
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from yolo.utils.logging_utils import ProgressLogger, log_model_structure
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from yolo.utils.model_utils import (
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ExponentialMovingAverage,
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self.validation_dataloader = create_dataloader(
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cfg.task.validation.data, cfg.dataset, cfg.task.validation.task, use_ddp
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)
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self.validator = ModelValidator(cfg.task.validation, cfg.dataset, model, vec2box, progress, device)
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if getattr(train_cfg.ema, "enabled", False):
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self.ema = ExponentialMovingAverage(model, decay=train_cfg.ema.decay)
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def __init__(
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self,
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validation_cfg: ValidationConfig,
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dataset_cfg: DatasetConfig,
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model: YOLO,
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vec2box: Vec2Box,
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progress: ProgressLogger,
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with contextlib.redirect_stdout(io.StringIO()):
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# TODO: load with config file
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json_path, _ = locate_label_paths(Path(dataset_cfg.path), dataset_cfg.get("val", "val"))
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if json_path:
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self.coco_gt = COCO(json_path)
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def solve(self, dataloader, epoch_idx=-1):
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# logger.info("🧪 Start Validation!")
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with open(self.json_path, "w") as f:
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json.dump(predict_json, f)
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if hasattr(self, "coco_gt"):
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self.progress.start_pycocotools()
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result = calculate_ap(self.coco_gt, predict_json)
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self.progress.finish_pycocotools(result, epoch_idx)
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return avg_mAPs
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yolo/utils/bounding_box_utils.py
CHANGED
@@ -376,7 +376,7 @@ def calculate_map(predictions, ground_truths, iou_thresholds=arange(0.5, 1, 0.05
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aps.append(ap)
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mAP = {
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"mAP.5":
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"mAP.5:.95": aps
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}
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return mAP
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aps.append(ap)
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mAP = {
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"mAP.5": aps[0],
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"mAP.5:.95": torch.mean(torch.stack(aps)),
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}
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return mAP
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yolo/utils/logging_utils.py
CHANGED
@@ -189,7 +189,7 @@ def validate_log_directory(cfg: Config, exp_name: str) -> Path:
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f"🔀 Experiment directory exists! Changed <red>{old_exp_name}</> to <green>{exp_name}</>"
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)
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save_path.mkdir(exist_ok=True)
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logger.opt(colors=True).info(f"📄 Created log folder: <u><fg #808080>{save_path}</></>")
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logger.add(save_path / "output.log", mode="w", backtrace=True, diagnose=True)
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return save_path
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f"🔀 Experiment directory exists! Changed <red>{old_exp_name}</> to <green>{exp_name}</>"
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)
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save_path.mkdir(parents=True, exist_ok=True)
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logger.opt(colors=True).info(f"📄 Created log folder: <u><fg #808080>{save_path}</></>")
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logger.add(save_path / "output.log", mode="w", backtrace=True, diagnose=True)
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return save_path
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